Kernelized fuzzy attribute C-means clustering algorithm

• Published in: Fuzzy sets and systems Vol. 159; no. 18; pp. 2428 - 2445
• Main Authors: Liu, Jingwei, Xu, Meizhi
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.09.2008; Elsevier
• Subjects: Applied sciences; Attribute means clustering; Circuit properties; Computer science; control theory; systems; Control theory. Systems; Digital circuits; Electric, optical and optoelectronic circuits; Electronic circuits; Electronics; Exact sciences and technology; Fuzzy C-means; Fuzzy clustering; Information, signal and communications theory; Kernelized fuzzy C-means; Mathematical methods; Miscellaneous; System theory; Telecommunications and information theory; Theoretical computing; Fuzzy clustering; Fuzzy C-means; Kernelized fuzzy C-means; Attribute means clustering; Kernel function; Engineering; Experimental result; Fuzzy system; Information processing; Database; Fuzzy set; Algorithm
• ISSN: 0165-0114; 1872-6801
• DOI: 10.1016/j.fss.2008.03.018

A novel kernelized fuzzy attribute C-means clustering algorithm is proposed in this paper. Since attribute means clustering algorithm is an extension of fuzzy C-means algorithm with weighting exponent m = 2 , and fuzzy attribute C-means clustering is a general type of attribute C-means clustering with weighting exponent m > 1 , we modify the distance in fuzzy attribute C-means clustering algorithm with kernel-induced distance, and obtain kernelized fuzzy attribute C-means clustering algorithm. Kernelized fuzzy attribute C-means clustering algorithm is a natural generalization of kernelized fuzzy C-means algorithm with stable function. Experimental results on standard Iris database and tumor/normal gene chip expression data demonstrate that kernelized fuzzy attribute C-means clustering algorithm with Gaussian radial basis kernel function and Cauchy stable function is more effective and robust than fuzzy C-means, fuzzy attribute C-means clustering and kernelized fuzzy C-means as well.